Scientific due diligence for humanitarian Disaster Risk Financing
This guide was developed by the Drought Risk Finance Science Laboratory (DRiSL) to provide a tool for both scientists and humanitarian practitioners to aid responsible and effective use of scientific data and modelling in humanitarian decision-making.
The guide aims to provide an interface between the “providers” of data and models and humanitarian organisations and at-risk groups – the “users” of the models to take decisions – giving each group a better understanding of the other. It supports users to ensure due care is taken in the design of their data and analytics in their DRF systems, and helps providers to better understand what will be needed for their work to be applied responsibly and effectively to achieve humanitarian goals.
The guide sets out a pathway of eight checkpoints for users and providers to take together. Working through this sequence of steps collaboratively will ensure the strongest datasets are selected and that basis risk is kept to a minimum. The result will be a more robust DRF system founded on the collective knowledge and expertise of both the practitioner and data scientist.
Using this guide will help you identify where data failures are most likely to occur, and under what conditions. It will help you bring to light sources of uncertainty and limitations so that they can be understood and hopefully, mitigated. The guide also aims to demonstrate why it might be appropriate to integrate a wider suite of data sources into your DRF system for different decision-making purposes.
This guide advocates that all learning gathered during testing and validation of a DRF system are communicated clearly and openly to all stakeholders so that they can understand and evaluate the system. It is an important principle of DRF that those who design the system are accountable and responsive to those who will ultimately rely on it.
Ultimately, the success of this work depends not just on providers and practitioners working together, but on the ability of humanitarian organisations to connect and collaborate with the at-risk people that their DRF systems seek to support. Effective anticipatory action demands a system-wide approach that integrates at-risk groups, who are at the forefront of both observing and generating risk information and responding to hazards. It is critical that these groups contribute to the quality of the data going into the DRF system, and are then able to see and use its outputs quickly and easily. Establishing a high level of community preparedness – the aim at the heart of DRF – can only be achieved through a two-way dialogue with local end-users.
The focus of this guide is on the work of data scientists and practitioners on the technical development of DRF systems but it should be read alongside: